Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust comparison of binary images
Pattern Recognition Letters
Distance-based functions for image comparison
Pattern Recognition Letters
Line-Based Recognition Using A Multidimensional Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human face recognition based on spatially weighted Hausdorff distance
Pattern Recognition Letters
Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
A new robust circular Gabor based object matching by using weighted Hausdorff distance
Pattern Recognition Letters
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Robust face imagematching under illumination variations
EURASIP Journal on Applied Signal Processing
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
An adaptive image Euclidean distance
Pattern Recognition
Pattern Recognition Letters
Hausdorff distance with k-nearest neighbors
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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Image matching has been a central problem in computer vision and image processing for decades. Most of the previous approaches to image matching can be categorized into the intensity-based and edge-based comparison. Hausdorff distance has been widely used for comparing point sets or edge maps since it does not require point correspondences. In this paper, we propose a new image similarity measure combining the Hausdorff distance with a normalized gradient consistency score for image matching. The normalized gradient consistency score is designed to compare the normalized image gradient fields between two images to alleviate the illumination variation problem in image matching. By combining the edge-based and intensity-based information for image matching, we are able to achieve robust image matching under different lighting conditions. We show the superior robustness property of the proposed image matching technique through experiments on face recognition under different lighting conditions.